alphafold

# AlphaFold2 Structure Prediction Use when the agent needs to run AlphaFold2 for protein structure prediction and complex modeling. Covers validating designed sequences, predicting binder-target complexes, and calculating confidence metrics (pLDDT, pTM, ipTM). Distinct from `alphafold-database` (which retrieves pre-computed structures) — this skill covers running AF2 predictions on custom sequences. ## Requirements - Python 3.8+ - CUDA 11.0+, 32 GB GPU VRAM minimum (A100 recommended) - For multimers: ColabFold recommended over local install ## Deployment Options ### 1. ColabFold (Recommended for Multimers) ```bash pip install colabfold[alphafold] # Single chain colabfold_batch input.fasta output_dir/ \ --model-type alphafold2_ptm \ --num-recycles 3 # Complex (multimer) — comma-separate chains in FASTA header # >complex:ChainA,ChainB colabfold_batch complex.fasta output_dir/ \ --model-type alphafold2_multimer_v3 \ --num-recycles 20 \ --num-models 5 ``` ### 2. LocalColabFold ```bash # Install wget https://raw.githubusercontent.com/YoshitakaMo/localcolabfold/main/install_colabbatch_linux.sh bash install_colabbatch_linux.sh # Run offline colabfold_batch sequences.fasta results/ \ --model-type alphafold2_multimer_v3 \ --num-recycles 3 \ --use-gpu-relax ``` ### 3. OpenFold (PyTorch reimplementation) ```bash pip install openfold python run_pretrained_openfold.py \ --fasta_paths input.fasta \ --output_dir results/ \ --model_device cuda:0 ``` ## Key Parameters | Parameter | Values | Notes | |-----------|--------|-------| | `--model-type` | `alphafold2_ptm`, `alphafold2_multimer_v3` | Use multimer for complexes | | `--num-recycles` | 3–20 | More recycles = better accuracy, slower | | `--num-models` | 1–5 | 5 models for ensemble confidence | | `--msa-mode` | `mmseqs2_uniref_env` (default), `single_sequence` | Single = no MSA, faster | | `--use-gpu-relax` | flag | Amber relaxation on GPU | ## Confidence Metrics ```python import numpy as np import json # Load result JSON with open("result_model_1.json") as f: result = json.load(f) plddt = np.array(result["plddt"]) # Per-residue confidence 0-100 ptm = result["ptm"] # Global TM-score estimate 0-1 iptm = result.get("iptm", None) # Interface TM-score (multimer only) pae = np.array(result.get("pae", [])) # Predicted Aligned Error matrix # Quality thresholds print(f"Mean pLDDT: {plddt.mean():.1f}") # >70 = good, >90 = excellent print(f"pTM: {ptm:.3f}") # >0.5 = confident fold if iptm: print(f"ipTM: {iptm:.3f}") # >0.6 = reliable complex, >0.8 = high confidence ``` ## Self-Consistency Validation for Designed Sequences ```bash # Design → predict → measure similarity to input backbone # 1. Generate sequences with ProteinMPNN # 2. Predict structure of each sequence with AF2 # 3. Calculate TM-score / RMSD vs. design backbone python3 -c " from Bio.PDB import PDBParser, Superimposer # Compare predicted vs. designed structure # High TM-score (>0.8) = sequence encodes target fold " ``` ## Output Files | File | Contents | |------|----------| | `*_relaxed_rank_1.pdb` | Top-ranked relaxed structure | | `*_unrelaxed_rank_1.pdb` | Top-ranked unrelaxed structure | | `result_model_*.json` | Scores: pLDDT, pTM, ipTM, PAE matrix | | `*_coverage.png` | MSA coverage plot | | `*_pae.png` | PAE heatmap (low = confident) | ## Quality Thresholds | Metric | Poor | Acceptable | Good | Excellent | |--------|------|-----------|------|-----------| | Mean pLDDT | <50 | 50–70 | 70–90 | >90 | | pTM | <0.4 | 0.4–0.5 | 0.5–0.7 | >0.7 | | ipTM (complex) | <0.5 | 0.5–0.6 | 0.6–0.8 | >0.8 | | Interface PAE | >20 Å | 15–20 Å | 8–15 Å | <8 Å | ## Common Issues | Problem | Cause | Fix | |---------|-------|-----| | Low ipTM despite high pLDDT | Chains fold well independently but don't interact | Redesign interface residues | | High PAE at interface | Interface not well-determined | Add more recycles; check contact predictions | | OOM on GPU | Sequence too long | Use `--chunk-size 128` or CPU for MSA | | All models disagree | Disordered region or wrong fold | Check MSA depth; try `--msa-mode single_sequence` |